Implementation of an Algorithm for Searching for Missing Units of a Swarm of Robots, Controlled by an Adapted Ant Algorithm

2021 ◽  
Author(s):  
Fedir V. Abramov ◽  
Alexander Andreiev ◽  
Olga Andreieva
Keyword(s):  
Ant Algorithm

Dynamic Optimization of Fed-Batch Bioreactors Using the Ant Algorithm

2001 ◽  
Vol 17 (1) ◽  
pp. 81-88 ◽  
Author(s):  
V.K. Jayaraman ◽  
B.D. Kulkarni ◽  
K. Gupta ◽  
J. Rajesh ◽  
H.S. Kusumaker
Keyword(s):  
Dynamic Optimization ◽  
Ant Algorithm ◽  
Fed Batch ◽  
Batch Bioreactors

scholarly journals Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Haipeng Peng ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
Shudong Li ◽  
Yixian Yang
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Complex Network ◽  
Parameter Optimization ◽  
Ant Algorithm ◽  
Structure Parameters ◽  
Topology Identification ◽  
Unknown Structure ◽  
Swarm Algorithm ◽  
Scientific Fields

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.

Ant-Based Clustering and Topographic Mapping

2006 ◽  
Vol 12 (1) ◽  
pp. 35-62 ◽  
Author(s):  
J. Handl ◽  
J. Knowles ◽  
M. Dorigo
Keyword(s):  
Real Data ◽  
Emergent Behavior ◽  
Ant Algorithm ◽  
Topographic Mapping ◽  
Sorting Algorithm ◽  
High Quality ◽  
Ant Colonies ◽  
Evaluation Functions ◽  
Data Collections ◽  
Standard Techniques

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.

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